{"id":"https://openalex.org/W2771292152","doi":"https://doi.org/10.1109/sdf.2017.8126349","title":"Bayesian processing of big data using log homotopy based particle flow filters","display_name":"Bayesian processing of big data using log homotopy based particle flow filters","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2771292152","doi":"https://doi.org/10.1109/sdf.2017.8126349","mag":"2771292152"},"language":"en","primary_location":{"id":"doi:10.1109/sdf.2017.8126349","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sdf.2017.8126349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100849116","display_name":"Muhammad Altamash Khan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Muhammad Altamash Khan","raw_affiliation_strings":["Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"],"affiliations":[{"raw_affiliation_string":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany","institution_ids":["https://openalex.org/I4210166245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027262906","display_name":"Allan De Freitas","orcid":"https://orcid.org/0000-0002-8552-481X"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Allan De Freitas","raw_affiliation_strings":["Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006505402","display_name":"Lyudmila Mihaylova","orcid":"https://orcid.org/0000-0001-5856-2223"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Lyudmila Mihaylova","raw_affiliation_strings":["Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048167927","display_name":"Martin Ulmke","orcid":"https://orcid.org/0000-0002-9714-1939"},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Ulmke","raw_affiliation_strings":["Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"],"affiliations":[{"raw_affiliation_string":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany","institution_ids":["https://openalex.org/I4210166245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040395718","display_name":"Wolfgang Koch","orcid":"https://orcid.org/0000-0002-5734-3325"},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfgang Koch","raw_affiliation_strings":["Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany"],"affiliations":[{"raw_affiliation_string":"Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany","institution_ids":["https://openalex.org/I4210166245"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100849116"],"corresponding_institution_ids":["https://openalex.org/I4210166245"],"apc_list":null,"apc_paid":null,"fwci":0.195,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63792365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8438177108764648},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.8321493864059448},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.677386462688446},{"id":"https://openalex.org/keywords/auxiliary-particle-filter","display_name":"Auxiliary particle filter","score":0.5771418809890747},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5279330611228943},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.522057056427002},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.5118500590324402},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4727628827095032},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45474904775619507},{"id":"https://openalex.org/keywords/homotopy","display_name":"Homotopy","score":0.45439839363098145},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.42982152104377747},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.3575153946876526},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2913753390312195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2132147252559662},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.1973077356815338},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.17047575116157532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12076559662818909},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.09289854764938354},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.07934343814849854}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8438177108764648},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.8321493864059448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.677386462688446},{"id":"https://openalex.org/C52483021","wikidata":"https://www.wikidata.org/wiki/Q4827310","display_name":"Auxiliary particle filter","level":5,"score":0.5771418809890747},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5279330611228943},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.522057056427002},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.5118500590324402},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4727628827095032},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45474904775619507},{"id":"https://openalex.org/C5961521","wikidata":"https://www.wikidata.org/wiki/Q746083","display_name":"Homotopy","level":2,"score":0.45439839363098145},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.42982152104377747},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3575153946876526},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2913753390312195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2132147252559662},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.1973077356815338},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.17047575116157532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12076559662818909},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.09289854764938354},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.07934343814849854},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/sdf.2017.8126349","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sdf.2017.8126349","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:121517","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Proceedings Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/400940","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/400940","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1599118604","https://openalex.org/W1624701674","https://openalex.org/W1827991045","https://openalex.org/W2056760934","https://openalex.org/W2113926057","https://openalex.org/W2114656557","https://openalex.org/W2140895908","https://openalex.org/W2144598923","https://openalex.org/W2262168898","https://openalex.org/W2963469257","https://openalex.org/W6635963195","https://openalex.org/W6636680426"],"related_works":["https://openalex.org/W2368144031","https://openalex.org/W3144709167","https://openalex.org/W2355962871","https://openalex.org/W2127981223","https://openalex.org/W2162253570","https://openalex.org/W2124156864","https://openalex.org/W4247602836","https://openalex.org/W2577958329","https://openalex.org/W2381817522","https://openalex.org/W2350937085"],"abstract_inverted_index":{"Bayesian":[0],"recursive":[1],"estimation":[2],"using":[3],"large":[4],"volumes":[5],"of":[6,48,84,105],"data":[7],"is":[8,45,75,88,101],"a":[9,91,132,163],"challenging":[10],"research":[11],"topic.":[12],"The":[13,39,98],"problem":[14],"becomes":[15],"particularly":[16],"complex":[17],"for":[18,113],"high":[19],"dimensional":[20],"non-linear":[21],"state":[22],"spaces.":[23],"Markov":[24],"chain":[25],"Monte":[26],"Carlo":[27],"(MCMC)":[28],"based":[29,140],"methods":[30,63,122],"have":[31],"been":[32],"successfully":[33],"used":[34],"to":[35,60,68,93,102,109],"solve":[36],"such":[37,73],"problems.":[38],"main":[40,99],"issue":[41],"when":[42],"employing":[43],"MCMC":[44,79],"the":[46,49,76,82,85,95,106,111,115,138,150],"evaluation":[47],"likelihood":[50,116],"function":[51],"at":[52],"every":[53],"iteration,":[54],"which":[55,114,137],"can":[56,152],"become":[57],"prohibitively":[58],"expensive":[59],"compute.":[61],"Alternative":[62],"are":[64,118],"therefore":[65],"sought":[66],"after":[67],"overcome":[69],"this":[70,128],"difficulty.":[71],"One":[72],"method":[74,92],"adaptive":[77,146],"sequential":[78],"(ASMCMC),":[80],"where":[81],"use":[83,104],"confidence":[86],"sampling":[87],"proposed":[89,158],"as":[90],"reduce":[94],"computational":[96],"cost.":[97],"idea":[100],"make":[103],"concentration":[107],"inequalities":[108],"sub-sample":[110],"measurements":[112],"terms":[117],"evaluated.":[119],"However,":[120],"ASMCMC":[121,134],"require":[123],"appropriate":[124],"proposal":[125],"distributions.":[126],"In":[127],"work,":[129],"we":[130],"propose":[131],"novel":[133],"framework":[135],"in":[136],"log-homotopy":[139],"particle":[141],"flow":[142],"filter":[143],"form":[144],"an":[145],"proposal.":[147],"We":[148],"show":[149],"performance":[151],"be":[153],"significantly":[154],"enhanced":[155],"by":[156],"our":[157],"algorithm,":[159],"while":[160],"still":[161],"maintaining":[162],"comparatively":[164],"low":[165],"processing":[166],"overhead.":[167]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
